README.md

---
language:
- en
- zh
- multilingual
license: apache-2.0
tags:
- GENIUS
- conditional text generation
- sketch-based text generation
- data augmentation
datasets:
- c4
- beyond/chinese_clean_passages_80m
widget:
- text: <mask> Conference on Empirical Methods <mask> submission of research papers
    <mask> Deep Learning <mask>
  example_title: Example 1
- text: <mask> machine learning <mask> my research interest <mask> data science <mask>
  example_title: Example 2
- text: <mask> play basketball <mask> a strong team <mask> Shanghai University of
    Finance and Economics <mask> last Sunday <mask>
  example_title: Example 3
- text: 'Good news: <mask> the European Union <mask> month by EU <mask> Farm Commissioner
    Franz <mask>'
  example_title: Example with a prompt 1
- text: 'Bad news: <mask> the European Union <mask> month by EU <mask> Farm Commissioner
    Franz <mask>'
  example_title: Example with a prompt 2
inference:
  parameters:
    max_length: 200
    num_beams: 3
    do_sample: true
---

# GENIUS: generating text using sketches!


- **Paper: [GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation](https://arxiv.org/abs/2211.10330)**
- **GitHub: [GENIUS, Pre-training/Data Augmentation Tutorial](https://github.com/beyondguo/genius)**





You can use this model directly with a pipeline for masked language modeling:

```python
from transformers import pipeline
# 1. load the model with the huggingface `pipeline`
genius = pipeline("text2text-generation", model='beyond/genius-large', device=0)
# 2. provide a sketch (joint by <mask> tokens)
sketch = "<mask> Conference on Empirical Methods <mask> submission of research papers <mask> Deep Learning <mask>"
# 3. here we go!
generated_text = genius(sketch, num_beams=3, do_sample=True, max_length=200)[0]['generated_text']
print(generated_text)
```

If you find our paper/code/demo useful, please cite our paper:
```
@article{guo2022genius,
  title={GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation},
  author={Guo, Biyang and Gong, Yeyun and Shen, Yelong and Han, Songqiao and Huang, Hailiang and Duan, Nan and Chen, Weizhu},
  journal={arXiv preprint arXiv:2211.10330},
  year={2022}
}
```